Electronic proceedings author index

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z
Andrea Finke
  • ESANN 2012 - Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces [Details]
Gavin Finnie
  • ESANN 2010 - Financial time series forecasting with machine learning techniques: a survey [Details]
Bettina Finzel
  • ESANN 2020 - Verifying Deep Learning-based Decisions for Facial Expression Recognition [Details]
S. Fiori
  • ESANN 1999 - A comparison of three PCA neural techniques [Details]
Fabrício Firmino
  • ESANN 2018 - Vector Field Based Neural Networks [Details]
Fabricio Firmino de Faria
  • ESANN 2019 - Memory Efficient Weightless Neural Network using Bloom Filter [Details]
Fabian Fischbach
  • ESANN 2025 - Encoding hyperspectral data with low-bond dimension quantum tensor networks [Details]
Asja Fischer
  • ESANN 2011 - Training RBMs based on the signs of the CD approximation of the log-likelihood derivatives [Details]
Volker Fischer
  • ESANN 2016 - Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks [Details]
  • ESANN 2017 - Learning Semantic Prediction using Pretrained Deep Feedforward Networks [Details]
  • ESANN 2018 - Hierarchical Recurrent Filtering for Fully Convolutional DenseNets [Details]
Lukas Fischer
  • ESANN 2023 - Secure Federated Learning with Kernel Affine Hull Machines [Details]
Lydia Fischer
  • ESANN 2014 - Rejection strategies for learning vector quantization [Details]
  • ESANN 2015 - Certainty-based prototype insertion/deletion for classification with metric adaptation [Details]
Asja Fischer
  • ESANN 2021 - SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations [Details]
Dalia Fishelov
  • ESANN 2016 - Auto-adaptive Laplacian Pyramids [Details]
William Fitzgerald
  • ESANN 2005 - A Class of Kernels For Sets of Vectors [Details]
Jeremy Fix
  • ESANN 2014 - Towards an effective multi-map self organizing recurrent neuronal network [Details]
Jeremy Fix
  • ESANN 2021 - Density Independent Self-organized Support for Q-Value Function Interpolation in Reinforcement Learning [Details]
Peter Flach
  • ESANN 2016 - Active transfer learning for activity recognition [Details]
  • ESANN 2018 - Anomaly detection in star light curves using hierarchical Gaussian processes [Details]
Manon Flageat
  • ESANN 2020 - Incorporating Human Priors into Deep Reinforcement Learning for Robotic Control [Details]
Remi Flamary
  • ESANN 2011 - Selecting from an infinite set of features in SVM [Details]
J.A. Flanagan
  • ESANN 1995 - Self-organisation, metastable states and the ODE method in the Kohonen neural network [Details]
  • ESANN 1998 - The self-organising map, robustness, self-organising criticality and power laws [Details]
  • ESANN 2000 - Self-Organisation in the SOM with a finite number of possible inputs [Details]
Lukas Fleckenstein
  • ESANN 2019 - Beta Distribution Drift Detection for Adaptive Classifiers [Details]
Andrew Fleming
  • ESANN 2010 - Validation of unsupervised clustering methods for leaf phenotype screening [Details]
F. Fleuret
  • ESANN 2002 - Theoretical properties of functional Multi Layer Perceptrons [Details]
P. Fleury
  • ESANN 2001 - Matching analogue hardware with applications using the Products of Experts algorithm [Details]
Arthur Flexer
  • ESANN 2014 - Choosing the Metric in High-Dimensional Spaces Based on Hub Analysis [Details]
Giuseppe Floris
  • ESANN 2023 - Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization [Details]
A.J.B. Fogg
  • ESANN 2002 - A data vizualisation method for investigating the reliability of a high-dimensional low-back-pain MLP network [Details]
Pierfrancesco Foglia
  • ESANN 2025 - Explainable ensemble learning for structural damage prediction under seismic events [Details]
Samuele Fonio
  • ESANN 2023 - Hierarchical priors for Hyperspherical Prototypical Networks [Details]
  • ESANN 2024 - FedHP: Federated Learning with Hyperspherical Prototypical Regularization [Details]
Jose Fonseca
  • ESANN 2013 - Are Rosenblatt multilayer perceptrons more powerfull than sigmoidal multilayer perceptrons? From a counter example to a general result [Details]
  • ESANN 2014 - A New Error-Correcting Syndrome Decoder with Retransmit Signal Implemented with an Hardlimit Neural Network [Details]